The recognition of cancer cells and regions of cancerous tissue is a key challenge in digital pathology. The applicants have proposed a method of tissue mapping to achieve this.
Digital Pathology, which can also be referred to as virtual microscopy or virtual pathology involves managing, analysing and interpreting digital information. The present disclosure relates to the application of methods of “machine vision” and “computerised image understanding” in tissue analysis and cancer detection. It also relates to multiresolution interrogation, and pattern driven analysis (e.g. the patterns in the data drive the processing functions) and selective image processing (reductions in global processing, and the selective application of different image processing function).
The process involves the generation of glass slides and converting these to digital pathology slides using digital pathology solutions. A digital slide scan is then generated which allows for high resolution viewing, interpretation and image analysis of digital pathology images. Growth in digital pathology solutions has totally transformed how research labs manage and interpret glass slides, with analysis and interpretation of histological images now conducted on a computer screen.
Gaining momentum globally, digital pathology is being used across health and pharmaceutical sectors, education and contract research organisations. With wide ranging applications the realised benefits of this sophisticated technology have encouraged high growth in the market for digital pathology solutions, which by 2020 is estimated to be worth $5.7 billion.
Whole slide imaging and digital pathology are significant ongoing fields of research. The methods in the present case contribute new developments in this field of technology. Cytonuclear analysis involves the automated recognition of cell boundaries and nuclear boundaries within Haematoxylin and Eosin stained tissue samples. This may be achieved by a process of colour deconvolution, and may be processed further to identify biological objects such as nuclei. In general, nuclei are the most easily identifiable components in Haematoxylin and Eosin stained tissue samples. This cytonuclear analysis is a key tool in providing accurate diagnoses.
The ability to provide accurate diagnosis is critical to the provision of healthcare. Biopsies to identify the presence of diseases such as cancer are a useful tool in such diagnosis. They may also enable predictions to be made about both future development of disease in a patient, and patient response to treatment in the context of precision or personalised medicine. Accurate prognostic assessment is also critically important, because it enables action to be taken to counteract further development of disease. Microscope images of tissue samples have been used for these purposes for many years.
Large numbers of microscope imaging systems, each with their own particular characteristics, have been developed for this purpose. Whole slide imaging systems obtain digital images of entire microscope slides by scanning the field of view of a microscope across a macroscopic tissue sample to obtain a series of digital images. The resulting digital images can then be concatenated together to provide a single image, or image set, which describes the entire microscope slide. Partial images of slides can also be obtained by the same approach.
Pathologists involved in making diagnoses based on these kinds of images may rely on qualitative judgements. Such judgements may be based on their scientific knowledge and also on personal experience. This is necessarily a subjective process. As a result diagnoses, prognostic assessments, predictive assessments, the selection of patients for clinical trials, and the discovery and validation of new biomarkers are not always reproducible—different pathologists may make different judgements based on identical images of tissue samples.
In making diagnostic judgements, the pathologist's task is made still more difficult because large tissue samples may be involved. In such cases many tens, or even hundreds of microscope images may need to be analysed from a single patient, and in some cases, it may be necessary to review regions of these multiple microscope images at multiple resolutions. This is particularly true where multiple tissue biopsies have been taken from a relatively large area of the body such as the prostate. These issues compound the problem of reproducibility because two different pathologists assessing the same patient's tissue sample may take into account features of different areas of different images of the same tissue sample.
The conditions under which a tissue sample was obtained, and the treatment of that sample before it was imaged (for example in terms of the concentration of stain applied to it), the imaging system used to acquire the image, and the presence of image artefacts may all cause variations between images. Although painstaking analysis is required, human pathologists are at least able intuitively to make allowances for such confounds. The subjective nature of assessment by human pathologists therefore, whilst problematic, at least provides one way to address these problems of inter-image variability. This need for intuitive judgement prevents straightforward automation of diagnostic and prognostic assessment of microscope images.
There are still further obstacles to overcome.